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RiskSpan Introduces Multi-Scenario Yield Table 

ARLINGTON, Va., August 4, 2022

RiskSpan, a leading provider of residential mortgage and structured product data and analytics, has announced a new Multi-Scenario Yield Table feature within its award-winning Edge Platform.  

REITs and other mortgage loan and MSR investors leverage the Multi-Scenario Yield Table to instantaneously run and compare multiple scenario analyses on any individual asset in their portfolio. 

An interactive, self-guided demo of this new functionality can be viewed here. 

Comprehensive details of this and other new capabilities are available by requesting a no-obligation live demo at riskspan.com. 

Request a No-Obligation Live Demo

With a single click from the portfolio screen, Edge users can now simultaneously view the impact of as many as 20 different scenarios on outputs including price, yield, WAL, dv01, OAS, discount margin, modified duration, weighted average CRR and CDR, severity and projected losses. The ability to view these and other model outputs across multiple scenarios in a single table eliminates the tedious and time-consuming process of running scenarios individually and having to manually juxtapose the resulting analytics.  

Entering scenarios is easy. Users can make changes to scenarios right on the screen to facilitate quick, ad hoc analyses. Once these scenarios are loaded and assumptions are set, the impacts of each scenario on price and other risk metrics are lined up in a single, easily analyzed data table. 

Analysts who determine that one of the scenarios is producing more reasonable results than the defined base case can overwrite and replace the base case with the preferred scenario in just two clicks.   

The Multi-Scenario Yield Table is the latest in a series of enhancements that is making the Edge Platform increasingly indispensable for mortgage loan and MSR portfolio managers. 


 About RiskSpan, Inc.  

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics. 

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments. 

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com.

Media contact: Timothy Willis 


It’s time to move to DaaS — Why it matters for loan and MSR investors

Data as a service, or DaaS, for loans and MSR investors is fast becoming the difference between profitable trades and near misses.

Granularity of data is creating differentiation among investors. To win at investing in loans and mortgage servicing rights requires effectively managing a veritable ocean of loan-level data. Buried within every detailed tape of borrower, property, loan and performance characteristics lies the key to identifying hidden exposures and camouflaged investment opportunities. Understanding these exposures and opportunities is essential to proper bidding during the acquisition process and effective risk management once the portfolio is onboarded.

Investors know this. But knowing that loan data conceals important answers is not enough. Even knowing which specific fields and relationships are most important is not enough. Investors also must be able to get at that data. And because mortgage data is inherently messy, investors often run into trouble extracting the answers they need from it.

For investors, it boils down to two options. They can compel analysts to spend 75 percent of their time wrangling unwieldy data – plugging holes, fixing outliers, making sure everything is mapped right. Or they can just let somebody else worry about all that so they can focus on more analytical matters.

Don’t get left behind — DaaS for loan and MSR investors

It should go without saying that the “let somebody else worry about all that” approach only works if “somebody else” possesses the requisite expertise with mortgage data. Self-proclaimed data experts abound. But handing the process over to an outside data team lacking the right domain experience risks creating more problems than it solves.

Ideally, DaaS for loan and MSR investors consists of a data owner handing off these responsibilities to a third party that can deliver value in ways that go beyond simply maintaining, aggregating, storing and quality controlling loan data. All these functions are critically important. But a truly comprehensive DaaS provider is one whose data expertise is complemented by an ability to help loan and MSR investors understand whether portfolios are well conceived. A comprehensive DaaS provider helps investors ensure that they are not taking on hidden risks (for which they are not being adequately compensated in pricing or servicing fee structure).

True DaaS frees up loan and MSR investors to spend more time on higher-level tasks consistent with their expertise. The more “blocking and tackling” aspects of data management that every institution that owns these assets needs to deal with can be handled in a more scalable and organized way. Cloud-native DaaS platforms are what make this scalability possible.

Scalability — stop reinventing the wheel with each new servicer

One of the most challenging aspects of managing a portfolio of loans or MSRs is the need to manage different types of investor reporting data pipelines from different servicers. What if, instead of having to “reinvent the wheel” to figure out data intake every time a new servicer comes on board, “somebody else” could take care of that for you?

An effective DaaS provider is one not only that is well versed in building and maintain loan data pipes from servicers to investors but also has already established a library of existing servicer linkages. An ideal provider is one already set-up to onboard servicer data directly onto its own DaaS platform. Investors achieve enormous economies of scale by having to integrate with a single platform as opposed to a dozen or more individual servicer integrations. Ultimately, as more investors adopt DaaS, the number of centralized servicer integrations will increase, and greater economies will be realized across the industry.

Connectivity is only half the benefit. The DaaS provider not only intakes, translates, maps, and hosts the loan-level static and dynamic data coming over from servicers. The DaaS provider also takes care of QC, cleaning, and managing it. DaaS providers see more loan data than any one investor or servicer. Consequently, the AI tools an experienced DaaS provider uses to map and clean incoming loan data have had more opportunities to learn. Loan data that has been run through a DaaS provider’s algorithms will almost always be more analytically valuable than the same loan data processed by the investor alone.  

Investors seeking to increase their footprint in the loan and MSR space obviously do not wish to see their data management costs rise in proportion to the size of their portfolios. Outsourcing to a DaaS provider that specializes in mortgages, like RiskSpan, helps investors build their book while keeping data costs contained.

Save time and money – Make better bids

For all these reasons, DaaS is unquestionably the future (and, increasingly, the present) of loan and MSR data management. Investors are finding that a decision to delay DaaS migration comes with very real costs, particularly as data science labor becomes increasingly (and often prohibitively) expensive.

The sooner an investor opts to outsource these functions to a DaaS provider, the sooner that investor will begin to reap the benefits of an optimally cost-effective portfolio structure. One RiskSpan DaaS client reported a 50 percent reduction in data management costs alone.

Investors continuing to make do with in-house data management solutions will quickly find themselves at a distinct bidding disadvantage. DaaS-aided bidders have the advantage of being able to bid more competitively based on their more profitable cost structure. Not only that, but they are able to confidently hone and refine their bids based on having a better, cleaner view of the portfolio itself.

Rethink your mortgage data. Contact RiskSpan to talk about how DaaS can simultaneously boost your profitability and make your life easier.

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Senior Home Equity Rises Again to $11.12 Trillion

Senior home equity rises again. Homeowners 62 and older saw their housing wealth grow by an estimated 4.9 percent ($520 billion) during the first quarter of 2022 to a record $11.1 trillion according to the latest quarterly release of the NRMLA/RiskSpan Reverse Mortgage Market Index.

Historical Changes in Aggregate Senior Home Values Q1 2000 - Q1 2022

The NRMLA/RiskSpan Reverse Mortgage Market Index (RMMI) rose to 388.83, another all-time high since the index was first published in 2000. The increase in older homeowners’ wealth was mainly driven by an estimated $563 billion (4.4 percent) increase in home values, offset by a $43 billion (2.1 percent) increase in senior-held mortgage debt.

For a comprehensive commentary, please see NRMLA’s press release.


How RiskSpan Computes the RMMI

To calculate the RMMI, RiskSpan developed an econometric tool to estimate senior housing value, mortgage balances, and equity using data gathered from various public resources. These resources include the American Community Survey (ACS), Federal Reserve Flow of Funds (Z.1), and FHFA housing price indexes (HPI). The RMMI represents the senior equity level at time of measure relative to that of the base quarter in 2000.[1] 

A limitation of the RMMI relates to Non-consecutive data, such as census population. We use a smoothing approach to estimate data in between the observable periods and continue to look for ways to improve our methodology and find more robust data to improve the precision of the results. Until then, the RMMI and its relative metrics (values, mortgages, home equities) are best analyzed at a trending macro level, rather than at more granular levels, such as MSA.


[1] There was a change in RMMI methodology in Q3 2015 mainly to calibrate senior homeowner population and senior housing values observed in 2013 American Community Survey (ACS).


Live Demo of RiskSpan’s Award-Winning Edge Platform

Wednesday, July 27th | 1:00 p.m. EDT

Register for the next Live Demo of RiskSpan’s award-winning Edge Platform. Learn more and ask questions at our bi-weekly, 45-minute demo.

Historical Performance Tool: Slice and dice historical loan performance in the Agency and PLRMBS universe to find outperforming cohorts.

Predictive Loan-Level Pricing and Risk Analytics: Produce loan-level pricing and risk on loans, MSRs, and structured products in minutes – with behavioral models applied at the loan-level, and other assumptions applied conveniently to inform bids and hedging.

Loan Data Management: Let RiskSpan’s data scientists consolidate and enhance your data across origination and servicing platforms, make it analytics-ready, and maintain if for ongoing trend analysis.


About RiskSpan:

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics.

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments.

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com.

Presenters

Joe Makepeace

Director, RiskSpan

Jordan Parker

Sales Executive, RiskSpan


RiskSpan Introduces Media Effect Measure for Prepayment Analysis, Predictive Analytics for Managed Data 

ARLINGTON, Va., July 14, 2022

RiskSpan, a leading provider of residential mortgage  and structured product data and analytics, has announced a series of new enhancements in the latest release of its award-winning Edge Platform.

Comprehensive details of these new capabilities are available byrequesting a no-obligation demo at riskspan.com.

Speak to An Expert

Media Effect – It has long been accepted that prepayment speeds see an extra boost as media coverage alerts borrowers to refinancing opportunities. Now, Edge lets traders and modelers measure the media effect present in any active pool of Agency loans—highlighting borrowers most prone to refinance in response to news coverage—and plot the empirical impact on any cohort of loans. Developed in collaboration with practitioners, it measures rate novelty by comparing rate environment at a given time to rates over the trailing five years. Mortgage portfolio managers and traders who subscribe to Edge have always been able to easily stratify mortgage portfolios by refinance incentive. With the new Media Effect filter/bucket, market participants fine tune expectations by analyzing cohorts with like media effects.

Predictive Analytics for Managed Data – Edge subscribers who leverage RiskSpan’s Data Management service to aggregate and prep monthly loan and MSR data can now kick off predictive analytics for any filtered snapshot of that data. Leveraging RiskSpan’s universe of forward-looking analytics, subscribers can generate valuations, market risk metrics to inform hedging, credit loss accounting estimates and credit stress test outputs, and more. Sharing portfolio snapshots and analytics results across teams has never been easier.

These capabilities and other recently released Edge Platform functionality will be on display at next week’s SFVegas 2022 conference, where RiskSpan is a sponsor. RiskSpan will be featured at Booth 38 in the main exhibition hall. RiskSpan professionals will also be available to respond to questions on July 19th following their panels, “Market Beat: Mortgage Servicing Rights” and “Technology Trends in Securitization.”


About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics.

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments.

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com.


Why Accurate Loan Pool and MSR Cost Forecasting Requires Loan-by-Loan Analytics

When it comes to forecasting loan pool and MSR cash flows, the practice of creating “rep lines,” or cohorts, of loans with similar characteristics for analytical purposes has its roots in the Agency MBS market. One of the most attractive and efficient features of Agencies is the TBA market. This market allows originators and issuers to sell large pools of mortgages that have not even been originated yet. This is possible because all parties understand what these future loans will look like. All these loans will all have enough in common as to be effectively interchangeable with one another.  

Institutions that perform the servicing on such loans may reasonably feel they can extend the TBA logic to their own analytics. Instead of analyzing a hundred similar loans individually, why not just lump them into one giant meta-loan? Sum the balances, weight-average the rates, terms, and other features, and you’re good to go. 

Why the industry still resorts to loan cohorting when forecasting loan pool and MSR cash flows

The simplest explanation for cohort-level analytics lies in its simplicity. Rep lines amount to giant simplifying assumptions. They generate fewer technological constraints than a loan-by-loan approach does. Condensing an entire loan portfolio down to a manageable number of rows requires less computational capacity. This takes on added importance when dealing with on-premise software and servers. It also facilitates the process of assigning performance and cost assumptions. 

What is more, as OAS modeling has evolved to dominate the loans and MSR landscape, the stratification approach necessary to run Monte Carlo and other simulations lends itself to cohorting. Lumping loans into like groups also greatly simplifies the process of computing hedging requirements. 

Advantages of loan-level over cohorting when forecasting cash flows

Treating loan and MSR portfolios like TBA pools, however, has become increasingly problematic as these portfolios have grown more heterogeneous. Every individual loan has a story. Even loans that resemble each other in terms of rate, credit score, LTV, DTI, and documentation level have unique characteristics. Some of these characteristics – climate risk, for example – are not easy to bucket. Lumping similar loans into cohorts also runs the risk of underestimating tail risk. Extraordinarily high servicing/claims costs on just one or two outlier loans on a bid tape can be enough to adversely affect the yield of an entire deal. 

Conversely, looking at each loan individually facilitates the analysis of portfolios with expanded credit boxes. Non-banks, which do not usually have the benefit of “knowing” their servicing customers through depository or other transactional relationships, are particularly reliant on loan-level data to understand individual borrower risks, particularly credit risks. Knowing the rate, LTV, and credit score of a bundled group of loans may be sufficient for estimating prepayment risk. But only a more granular, loan-level analysis can produce the credit analytics necessary to forecast reliably and granularly what a servicing portfolio is really going to cost in terms of collections, loss mitigation, and claims expenses.  

Loan-level analysis also eliminates the reliance on stratification limitations. It facilitates portfolio composition analysis. Slicing and dicing techniques are much more simply applied to loans individually than to cohorts. Looking at individual loans also reduces the risk of overrides and lost visibility into convexity pockets. 

Loan-Level MSR Analytics

Potential challenges and other considerations 

So why hasn’t everyone jumped onto the loan-level bandwagon when forecasting loan pool and MSR cash flows? In short, it’s harder. Resistance to any new process can be expected when existing aggregation regimes appear to be working fine. Loan-level data management requires more diligence in automated processes. It also requires the data related to each individual loan to be subjected to QC and monitoring. Daily hedging and scenario runs tend to focus more on speed than on accuracy at the macro level. Some may question whether the benefits of such a granular, case-by-case analysis that identifying the most significant loan-level pickups requires actually justifies the cost of such a regime. 

Rethink. Why now? 

Notwithstanding these challenges, there has never been a better time for loan and MSR investors to abandon cohorting and fully embrace loan-level analytics when forecasting cash flows. The emergence of cloud-native technology and enhanced database and warehouse infrastructure along with the ability to outsource the hosting and computational requirements out to third parties creates practically limitless scalability. 

The barriers between loan and MSR experts and IT professionals have never been lower. This, combined with the emergence of a big data culture in an increasing number of organizations, has brought the granular daily analysis promised by loan-level analytics tantalizingly within reach.  

 

For a deeper dive into loan and MSR cost forecasting, view our webinar, “How Much Will That MSR Portfolio Really Cost You?”

 


RiskSpan Introduces Proprietary Measure for Plotting Burnout Effect on Prepays, Adds RPL/NPL Forecasting

ARLINGTON, Va., June 22, 2022 —

RiskSpan, a leading provider of residential mortgage and structured product data and analytics, has announced a series of new enhancements in the latest release of its award-winning Edge Platform.  

Comprehensive details of these new capabilities are available by requesting a no-obligation demo at riskspan.com.

  • Burnout Metrics MBS traders and investors can now look up a proprietary, cumulative burnout metric that quantifies the extent to which a defined pool of mortgages has continued to pay coupons above refinance rates over time. The metric goes beyond simple comparisons of note rates to historic prevailing rates by also tracking the number of times borrowers have ignored the “media effect” of repeatedly seeing rates reach record lows. Edge users can plot empirical prepay speeds as a function of burnout to help project performance of pools with various degrees of burnout. A virtual walk-through of this functionality is available here.
  • Reperforming Loans Investors in nonperforming and reperforming loans – particularly RPLs that have recently emerged from covid forbearance – can now project performance and cash flows of loans with deferred balances. Edge reads in the total debt owed (TDO) recovery method and has added key output fields like prepaid principal percent reduction and total debt owed to its cash flow report.
  • Hedge Ratios – The Edge Platform now enables traders and portfolio managers to easily compute, in one single step, the quantity of 2yr, 5yr, 10yr, or 30yr treasuries (or any combination of these or other hedges) that must be sold to offset the effective duration of assets in a given portfolio. Swaps, swaptions and other hedges are also supported. Clearly efficient and useful for any portfolio of interest-rate-sensitive assets, the functionality is proving particularly valuable to commercial banks with MSR holdings and others who require daily transparency to hedging ratios.  

### 

About RiskSpan, Inc. 

RiskSpan offers end-to-end solutions for data management, historical performance, predictive analytics and portfolio risk management on a secure, fast, and scalable platform that has earned the trust of the industry’s largest firms. Combining the strength of subject matter experts, quantitative analysts, and technologists, RiskSpan’s Edge platform integrates a range of datasets – structured and unstructured – and off-the-shelf analytical tools to provide you with powerful insights and a competitive advantage. Learn more at www.riskspan.com.  

SPEAK to An EXPERT

Webinar Recording: How Much Will That MSR Portfolio Really Cost You?

Recorded: June 8th | 1:00 p.m. ET

Accurately valuing a mortgage servicing rights portfolio requires accurately projecting MSR cash flows. And accurately projecting MSR cash flows requires a reliable forecast of servicing costs. Trouble is, servicing costs vary extensively from loan to loan. While the marginal cost of servicing a loan that always pays on time is next to nothing, seriously delinquent loans can easily cost hundreds, if not thousands, of dollars per year.

The best way to account for this is to forecast and assign servicing costs at the loan level – a once infeasible concept that cloud-native technology has now brought within reach. Our panelists present a novel, granular approach to servicing cost analytics and how to get to a truly loan-by-loan MSR valuation (without resorting to rep lines).

 

Featured Speakers

Venkat Mullur

SVP, Capital Markets, Ocwen

Paul Gross

Senior Quantitative Analyst, New Residential Investment Corp.

Dan Fleishman

Managing Director, RiskSpan

Joe Makepeace

Director, RiskSpan


Striking a Proper Balance: ESG for Structured Finance

The securitization market continues to wrestle with the myriad of approaches and lack of standards in identifying and reporting ESG factors in transactions and asset classes. But much needed guidance is on the way as industry leaders work toward a consensus on the best way to report ESG for structured finance.  

RiskSpan gathered with other key industry players tackling these challenges at this month’s third annual Structured Finance Association ESG symposium in New York City. The event identified a number of significant strides taken toward shaping an industry-standard ESG framework and guidelines.  

Robust and engaging discussions across a variety of topics illustrated the critical need for a thoughtful approach to framework development. We observed a broad consensus around the notion that market acceptance would require any solution to be data supported and fully transparent. 

Much of the discussion revolved around three recurring themes: Finding a workable balance between the institutional desire for portfolio-specific measures based on raw data and the market need for a standardized scoring mechanism that everybody understands, maintaining data privacy, and assessing tradeoffs between the societal benefits of ESG investing and the added risk it can pose to a portfolio. 

Striking the Right Balance: Institution-Specific Measures vs. Industry-Standard Asset Scoring 

When it comes to disclosure and reporting, one point on a spectrum does not fit all. Investors and asset managers vary in their ultimate reporting needs and approach to assessing ESG and impact investing. On the one hand, having raw data to apply their own analysis or specific standards can be more worthwhile to individual institutions. On the other, having well defined standards or third-party ESG scoring systems for assets provides greater certainty and understanding to the market as a whole.  

Both approaches have value.

Everyone wants access to data and control over how they view the assets in their portfolio. But the need for guidance on what ESG impacts are material and relevant to structured finance remains prominent. Scores, labels, methodologies, and standards can give investors assurance a security contributes to meeting their ESG goals. Investors want to know where their money is going and if it is meaningful.

Methodologies also have to be explainable. Though there was agreement that labeled transactions are not always necessary (or achievable), integration of ESG factors in the decision process is. Reporting systems will need to link underlying collateral to external data sources to calculate key metrics required by a framework while giving users the ability to drill down to meet specific and granular analytical needs.    

Data Privacy

Detailed analysis of underlying asset data, however, highlights a second key issue: the tradeoff between transparency and privacy, particularly for consumer-related assets. Fiduciary and regulatory responsibility to protect disclosure of non-public personally identifiable information limits investor ability to access loan-level data.

While property addresses provide the greatest insight to climate risk and other environmental factors, concerns persist over methods that allow data providers to triangulate and match data from various sources to identify addresses. This in turn makes it possible to link sensitive credit information to specific borrowers.

The responsibility to summarize and disclose metrics required by the framework falls to issuers. The largest residential issuers already appreciate this burden. These issuers have expressed a desire to solve these issues and are actively looking at what they can do to help the market without sacrificing privacy. Data providers, reporting systems, and users will all need to consider the guardrails needed to adhere to source data terms of use.   

Assessing Impact versus Risk

Another theme arising in nearly all discussions centered on assessing ESG investment decisions from the two sometimes competing dimensions of impact and risk and considering whether tradeoffs are needed to meet a wide variety of investment goals. Knowing the impact the investment is making—such as funding affordable housing or the reduction of greenhouse gas emissions—is fundamental to asset selection or understanding the overall ESG position.

But what risks/costs does the investment create for the portfolio? What is the likely influence on performance?

The credit aspect of a deal is distinct from its ESG impact. For example, a CMBS may be socially positive but rent regulation can create thin margins. Ideally, all would like to maximize positive impact but not at the cost of performance, a strategy that may be contributing now to an erosion in greeniums. Disclosures and reporting capabilities should be able to support investment analyses on these dimensions.  

A disclosure framework vetted and aligned by industry stakeholders, combined with robust reporting and analytics and access to as much underlying data as possible, will give investors and asset managers certainty as well as flexibility to meet their ESG goals.   

Contact us

Webinar: Tailoring Stress Scenarios to Changing Risk Environments

July 13th | 1:00 p.m. ET

Designing market risk stress scenarios is challenging because of the disparate ways in which various risk factors impact different asset classes. No two events are exactly alike, and the Covid-19 pandemic and the Russian invasion of Ukraine each provide a case study for risk managers seeking to incorporate events without precise precedents into existing risk frameworks.
 
Join RiskSpan’s Suhrud Dagli and Martin Kindler on Wednesday, June 15th at 1 p.m. ET as they illustrate an approach for correlating rates, spreads, commodity prices and other risk factors to analogous historical geopoltical disruptions and other major market events. Market risk managers will receive an easily digestable tutorial on the math behind how to create probability distributions and reliably model how such events are most likely to impact a portfolio.

 

Featured Speakers

Suhrud Dagli

Co-Founder and CIO, RiskSpan

Photo of Martin Kindler

Martin Kindler

Managing Director, RiskSpan


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